Yajuan Lv

Phrase Filtering for Content Words in Hierarchical Phrase-Based Model

When hierarchical phrase-based statistical machine translation systems are used for language translation, sometimes the translations’ content words were lost: source-side content words is empty when translated into target texts during decoding. Although the translations’ BLEU score is very high, it is difficult to understand the translations because of the loss of the content words. In this paper, we propose a basic and efficient method for phrase filtering, with which the phrase’ content words translation are checked to decide whether to use the phrase in decoding or not. The experimental results show that the proposed method alleviates the problem of the loss content words’ and improves the BLEU scores.

A Simple, Fast Strategy for Weighted Alignment Hypergraph

Weighted alignment hypergraph [4] is potentially useful for statistical machine translation, because it is the first study to simultaneously exploit the compact representation and fertility model of word alignment. Since estimating the probabilities of rules extracted from hypergraphs is an NP-complete problem, they propose a divide-and-conquer strategy by decomposing a hypergraph into a set of independent subhypergraphs. However, they employ a Bull’s algorithm to enumerate all consistent alignments for each rule in each subhypergraph, which is very time-consuming especially for the rules that contain non-terminals. This limits the applicability of this method to the syntax translation models, the rules of which contain many non-terminals (e.g. SCFG rules). In response to this problem, we propose an inside-outside algorithm to efficiently enumerate the consistent alignments. Experimental results show that our method is twice as fast as the Bull’s algorithm. In addition, the efficient dynamic programming algorithm makes our approach applicable to syntax-based translation models.